System for analysis and prediction of financial and statistical data

ABSTRACT

System for the analysis and prediction of the evolution of various types of data such as stock market prices, financial indices and other statistical data, based on the presence of characteristic figures in a dense network of curves constructed from data.

FIELD OF INVENTION

The present invention relates to the analysis and prediction of theevolution of various types of data such as stock market prices,financial indices and other statistical data.

BACKGROUND OF THE INVENTION

Currently, the following two methods are used to analyze and predictdata in the field of finance and economics:

-   -   Technical analysis, based exclusively on the examination of a        small number of technical indicators derived from the given        data;    -   Fundamental analysis, based on knowledge of the economic        situation with regard to the data considered.        These two approaches often result in predictions that not only        differ, but are also often invalidated afterward.

SUMMARY OF THE INVENTION

The present system allows for a superior level of analysis andprediction of the evolution of the aforementioned data, bothqualitatively and quantitatively. It rests primarily on a dense networkof curves constructed mathematically from numerical data (for example, astock price) and defined by a primary parameter (the number of datapoints used) and a secondary parameter (the scale parameter). A computeris used to receive and process the data.

The curves of this network belong to one of the following categories:

-   Moving regression (MR) of degree zero, known as the moving average    (MA);-   MR of the first degree, known as the moving linear regression (MLR);-   MR of the second degree, which we will call the moving quadratic    regression (MQR);-   MR of the k^(th) degree, which we will call the moving k regression    (MKR).

The MA is a well-known indicator commonly used in technical analysis.The MLR, a known but seldom used technical indicator, is built upon thelinear regression according to a defined method. The MQR is built uponthe quadratic regression according to the same method. The MKR is builtsimilarly upon a regression of the k^(th) degree.

The present system is fundamentally based on the utilization of a densenetwork of MRs corresponding to a large set of values of the primaryparameter, chosen according to defined criteria.

When MLRs are used to construct the dense network, characteristicfigures appear strikingly on the monitor of a computer. For this reasonand others that will be discussed later, the network described in whatfollows is composed of MLRs. It is on the presence of thesecharacteristic figures within the dense network that rests the abilityto obtain precise and reliable information on the evolution of the dataunder consideration.

The system can also use adjusted data, for example, averaged or weighteddata.

The secondary parameter (the scale parameter) can be the interval oftime separating two consecutive data points, for example, minutes, hoursor days. Other types of intervals can also be used; for a financialmarket, for example, the interval can be expressed in terms of thenumber of exchanges.

The necessary conditions under which the characteristic figures appearin the network are the following:

-   1) The network must contain a large number of MLRs, greater than    about 20. For these characteristic figures to be better observed,    ideally, this number must be greater than 100;-   2) The set of the values of the primary parameter must extend over a    sufficiently large range;-   3) The distribution of the values of the primary parameter must be    such that the corresponding network has a uniform density on    average.

In practice, criterion 3) is satisfied when the values of the primaryparameter constituting the set grow slowly and uniformly. Furthermore,if wished, one can slightly modify the density, for example, by makingthe network denser for smaller values of the primary parameter.

The following algebraic formula is used to determine with more thansufficient precision the values of the primary parameter, including thepossibility of modifying the density:$n_{k} = {n_{1} + {\left( {k - 1} \right)a} + {\frac{k\left( {k - 1} \right)}{N\left( {N - 1} \right)}\left\lbrack {n_{N} - n_{1} - {\left( {N - 1} \right)a}} \right\rbrack}}$where:

-   k ={1, . . . N};-   N is the number of curves in the network;-   n₁ is the first term of the set;-   n_(N) is the N^(th) term of the set; and-   a is the interval between n₁ and n₂.

Taking N =100, n₁=8, n_(N)=1502, and a =8 as an example, one obtains forthe primary parameter the following set of values:

-   {8, 16, 24, 33, 41, 50, 59, 68, . . . , 1351, 1372, 1393, 1415,    1436, 1458, 1480, 1502} This set of values generates a network of    100 MLRs which, as desired, has a uniform density on average and    extends over a large range.

The characteristic figures seen on the monitor of the computer belong toone of the following three types:

-   1) Cords;-   2) Envelopes;-   3) Boltropes.

A cord is a pronounced condensation of curves that stands out from aless dense background of curves of the network.

An envelope outlines the boundary of a group of curves of the network.

A boltrope is both a cord and an envelope.

A characteristic figure attracts or repels the representative curve ofthe data, depending on its type, its shape and its relative position tothe representative curve of the data. The more marked the characteristicfigure, the stronger the attraction or the repulsion.

The analysis and prediction of the evolution of the data requires theexamination of the ensemble of the cords, envelopes and boltropes andthe representative curve of the data up to a given moment, over asufficiently large interval of consecutive data points. An interval isconsidered sufficiently large when it contains a peripheralcharacteristic figure at the top of the network exhibiting an convexupward turning point and another one at the bottom exhibiting a convexdownward turning point. The ensemble of the cords, envelopes andboltropes and the representative curve of the data up to a given momentobserved over a sufficiently large interval is referred to as a ‘spatialconfiguration’.

Qualitative and quantitative indications are obtained from a givenspatial configuration by determining which characteristic figuresspecifically attract and which characteristic figures specifically repelthe representative curve of the data, and this is achieved through theexamination of numerous and varied past spatial configurations and theirsubsequent evolutions.

The reasons for which the MLR has been chosen, as mentioned above, areas follows:

-   Characteristic figures do not appear within MAs networks;-   Characteristic figures appear clearly within MLRs networks which can    be implemented on last-generation PCs;-   MKRs networks, starting with MQRS, are difficult to implement on    last-generation PCs, due to limited processing capabilities.

The fact that characteristic figures appear within the network,regardless of the value of the scale parameter, can be exploited tobroaden the spectrum of analysis and prediction.

The readability of the graphical display of the network and therepresentative curve of the data can be improved by using differentcolors.

1. A system for the analysis and prediction of the evolution of varioustypes of data such as stock market prices, financial indices and otherstatistical data, characterized by a dense network of curves constructedmathematically from such data, in which characteristic figures appear.2. A system according to claim 1, wherein the curves of the network aremoving linear regressions or moving regressions other than moving linearregressions.
 3. A system according to claim 1, wherein the analysis andprediction of the evolution of the data is achieved through observingthe way in which the representative curve of the data is attracted orrepelled by the characteristic figures.
 4. A system according to claim1, wherein for the considered data more than one network with differentscale parameter values are displayed.
 5. A system according to claim 1,wherein multiple colors are used for the display of the network and therepresentative curve of the data.